Space debris removal is currently a critical issue for space development.It has been reported that five pieces of debris should be removed each year to avoid further increase in the amount of debris in orbit.One approach for the removal of multiple pieces of debris is to launch multiple satellites that can each remove one target debris from orbit.The benefit of this approach is that the target debris can be removed without orbit transition, and thus, the satellite can be developed considering simple satellite mechanics.However, to realize this concept, multiple satellites need to be launched.Another approach is to use one satellite to remove multiple pieces of space debris.This approach can reduce the launch costs and achieve efficient removal of space debris.However, the satellite must change its orbit after the removal of each debris piece, and a technique for optimizing the orbit transition is required.In this study, the latter strategy and developed a satellite trajectory optimization method for efficient space debris removal were focused on.The similarity between the problem of multiple space debris removal and the travelling serviceman problem (TSP) were considered, and the TSP solution involving an evolutionary algorithm (EA) was applied.To improve the efficiency of multiple debris removal, the total radar cross-section (RCS), which indicates the amount of space debris, and the total thrust of the satellite was minimized.The TSP solution method was extended to multiple objectives by coupling it with a satellite trajectory simulation.To evaluate the developed method, a set of 100 pieces of space debris was selected from a database.The results indicated a trade-off between the total RCS and total thrust.
A single-stage launch vehicle with hybrid rocket engine, which uses solid fuel and liquid oxidizer, has been being studied and developed as a next-generation rocket for scientific observation due to the advantages as low cost, safety, re-ignition, and reduced pollution. Therefore, the knowledge regarding hybrid rocket system has been being gained through the forepart of the conceptual design using design informatics. In the present study, the practical problem defined by using three objective functions and seven design variables for aurora observation is treated so as to contribute the real world using evolutionary computation and data mining for the field of aerospace engineering. The primary objective of the design in the present study is that the down range and the duration time in the lower thermosphere are sufficiently obtained for the aurora scientific observation, whereas the initial gross weight is held down. Investigated solid fuels are five, while liquid oxidizer is considered as liquid oxygen. The condition of single-time ignition is assumed in flight sequence in order to quantitatively investigate the ascendancy of multi-time ignition. A hybrid evolutionary computation between the differential evolution and the genetic algorithm is employed for the multidisciplinary design optimization. A self-organizing map is used for the data mining technique in order to extract global design information. Consequently, the design information regarding the tradeoffs among the objective functions, the behaviors of the design variables in the design space to become the nondominated solutions, and the implication of the design variables for the objective functions have been obtained in order to quantitatively differentiate the advantage of hybrid rocket engine in view of the five fuels. Moreover, the next assignments were also revealed.
The multi-objective genetic algorithm (MOGA) is applied to the multi-disciplinary conceptual design problem for a three-stage launch vehicle (LV) with a hybrid rocket engine (HRE). MOGA is an optimization tool used for multi-objective problems. The parallel coordinate plot (PCP), which is a data mining method, is employed in the post-process in MOGA for design knowledge discovery. A rocket that can deliver observing micro-satellites to the sun-synchronous orbit (SSO) is designed. It consists of an oxidizer tank containing liquid oxidizer, a combustion chamber containing solid fuel, a pressurizing tank and a nozzle. The objective functions considered in this study are to minimize the total mass of the rocket and to maximize the ratio of the payload mass to the total mass. To calculate the thrust and the engine size, the regression rate is estimated based on an empirical model for a paraffin (FT-0070) propellant. Several non-dominated solutions are obtained using MOGA, and design knowledge is discovered for the present hybrid rocket design problem using a PCP analysis. As a result, substantial knowledge on the design of an LV with an HRE is obtained for use in space transportation.
Space debris mitigation is a key technology for space development. Further increase in the amount of debris can be avoided if five pieces of debris is removed every year. One concept to remove multiple pieces of debris is to use a satellite. This approach can reduce the launch cost and remove space debris efficiently compared to using multiple satellite that removes one piece of debris. To realize this concept, an optimization technique for orbit transition is required. This study develops a satellite trajectory optimization using evolutionary algorithms (EAs). The travelling serviceman problem's (TSP) solution of EA is applied considering the similarity between the two. The TSP solution method is extended by coupling it with a satellite trajectory simulation. To improve the efficiency for multiple debris removal, the maximization of the total radar cross-section (RCS) is considered that indicates the amount of space debris as an objective function. The total fuel consumption of the satellite is calculated by considering the total velocity increment as a constraint. To evaluate the developed method, a set of 2000 pieces of space debris were selected from a database, and five cases were solved by changing the total velocity increment by 20 m/s, 40 m/s, 60 m/s, and infinity. As a result, RCS was reduced as the total velocity increments were reduced. Trends of solutions obtained through the EA process were visualized using scatter plot matrix.
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